这篇论文搞了个叫CARP的机制，不用知道真相就能让爱撒谎的AI商家少卖货，配上SPARC效果更好，挺有意思。
LLM代理作为商家，即使被指示诚实，在大多数商品描述中仍会编造属性以赢得销售。平台无法逐条核实真伪，只能收到带噪声的投诉信号。研究者提出CARP机制，用死区容忍投诉噪声、状态依赖严重性抵抗检测侵蚀，无需真实值即可让低评分撒谎者的销量下降。搭配SPARC后，消费者福利接近完美信息基准，且优于其他对照策略。行为实验显示，商家在撒谎导致销量损失时会主动克制，说明惩罚在行为层面生效。
Paying for Honesty Without Knowing the Truth: Reputation-Penalty Design for LLM Marketplace Agents
LLM agents increasingly act as autonomous merchants that write their own product listings, and under competitive pressure, they fabricate attributes to win sales. Even under instructions to be honest, they fabricate attributes in a majority of listings across models. A platform's obvious remedy---verifying each claim against the truth---is unavailable, because it observes only a noisy, biased complaint signal, never the ground truth. We design CARP, a reputation-penalty mechanism with a deadband that forgives complaint noise and a state-dependent severity that counters reputation-driven detection erosion. CARP requires no product-level ground truth and is robust to strategic gaming. CARP protects consumers by suppressing the sales volume of low-rated liars while sparing honest sellers. Paired with SPARC, it closes most of the consumer-welfare gap relative to a perfect-information oracle, without ever accessing the truth. It also achieves the best welfare of the policies we compare. We further show that this felt penalty becomes behaviorally binding through SPARC, a byte-clean code-gated reflection mechanism: LLM merchants fabricate when lying is free but restrain themselves when fabrication costs them sales, a self-interested response rather than compliance. We trace this distinction to penalty-gated self-correction reasoning, and observe the binding across models, with supporting confidence intervals.